Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models

Abstract

Significance: Functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brains. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy. Aim: We aim to highlight the importance of accurate anatomical modeling in the context of fNIRS and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis. Approach: We have developed a surface-based brain meshing pipeline that can produce significantly better brain mesh models, compared to conventional meshing techniques. It can convert segmented volumetric brain scans into multilayered surfaces and tetrahedral mesh models, with typical processing times of only a few minutes and broad utilities, such as in Monte Carlo or finite-element-based photon simulations for fNIRS studies. Results: A variety of high-quality brain mesh models have been successfully generated by processing publicly available brain atlases. In addition, we compare three brain anatomical models-the voxel-based brain segmentation, tetrahedral brain mesh, and layered-slab brain model-and demonstrate noticeable discrepancies in brain partial pathlengths when using approximated brain anatomies, ranging between −1.5% to 23% with the voxelated brain and 36% to 166% with the layered-slab brain. Conclusion: The generation and utility of high-quality brain meshes can lead to more accurate brain quantification in fNIRS studies. Our open-source meshing toolboxes "Brain2Mesh" and "Iso2Mesh" are freely available at http://mcx.space/brain2mesh.

Investigating Developmental Changes in Scalp-Cortex Correspondence Using Diffuse Optical Tomography Sensitivity in Infancy

“…simulations in all individual MRIs and age-appropriate average templates and MMC simulationsTran, Yan, & Fang, 2020;Yan et al, 2019) in 3-month and 6-month old individual MRIs and average templates. We computed the Direct DOT and S-D Channel DOT distances using the photon fluence estimated from the simulations.…”

A hybrid mesh and voxel based Monte Carlo algorithm for accurate and efficient photon transport modeling in complex bio-tissues

“…10(a) in Ref. [7]) and was used to illustrate both Type I and II errors in [7]. Additionally, we report several key optical parameters derived from the simulations and compare those with DMMC [21] and MCX reported previously (see Table 1 in Ref.…”

“…There are several known approaches to restore curved boundary information using a voxelated data array. In one approach, one can apply a surface extraction algorithm such as -sampling, as used in our brain mesh generation pipeline [7]. However, these extracted surface triangles are not bounded by the containing voxel.…”

“…In recent years, several studies have pointed out that the orthogonal axis-aligned boundaries in a voxelated domain could produce unexpected modeling errors due to the unique optical characteristics associated with the special orientations of the boundary facets [6]. Recently, in [7], we have identified two types of artifacts that may arise when adopting a voxelized domain representation in MC. First, rasterization of a curved surface results in deviations between the original surface and a voxelated surface, as shown in Fig.…”

“…By further tessellating the domains between triangular surfaces, the mesh-based MC (MMC) using tetrahedral meshes was further proposed [14,15] to dramatically accelerate the ray-triangle intersection testing. However, creating anatomically accurate tetrahedral mesh is not a trivial task and often requires many careful considerations and dedicated meshing tools [7]. Furthermore, a mesh based data structure is significantly more complex and memory-expensive when compared to VMC.…”